In data analysis, it is often desirable to identify trends in time series data. For example, automated monitoring of network performance may generate time series data for a large number of monitored variables. When an anomaly is identified for one of the monitored network performance variables and/or resulting time series data, an information technology (IT) specialist may want to identify what else occurred at the same time for other variables/time series data. By looking at the data for the other variables/time series data, the IT specialist may be able to identify the root cause of the anomaly.
In current approaches, the IT specialist can view time series charts for multiple variables/time series data and manually determine whether any correlation may be present between two or more of the charts. However, particularly when a large number of variables are monitored, such an approach is cumbersome and subject to human error. Other approaches enable a user to designate a monitored time period to be analyzed, and a computer will analyze the same time period for other variables to determine if a similar anomaly was present. The computer can present a list of the variables/time period charts that exhibit a similar or correlated anomaly.